Network management actions based on access point classification

ABSTRACT

An example system includes access point (AP) devices configured to provide a wireless network at a site; and a network management system that stores network data received from the AP devices, the network data collected by the AP devices or client devices associated with the wireless network, and one or more processors configured to: receive a time series of SLE metrics based on the network data, determine, based on the time series, whether a network event has occurred, in response to a determination that a network event has occurred, determine a root cause for the network event, and in response to a determination that the root cause of the network event is associated with an AP device, determine a classification of the AP device, and determine a network management action for the AP device based on the network event and the classification of the AP device.

PRIORITY CLAIM

This application claims priority to U.S. application Ser. No.17/402,215, filed Aug. 13, 2021, entitled “NETWORK MANAGEMENT ACTIONSBASED ON ACCESS POINT CLASSIFICATION,” the entire content of which isincorporated herein by reference.

FIELD

The disclosure relates generally to computer networks and, morespecifically, automated techniques for performing network managementactions based on a classification of an access point.

BACKGROUND

Wireless access networks make use of network of wireless access points(APs), which are physical, electronic devices that enable other devicesto wirelessly connect to a wired network using various wirelessnetworking protocols and technologies, such as wireless local areanetworking protocols conforming to one or more of the IEEE 802.11standards (i.e., “Wi-Fi”), Bluetooth/Bluetooth Low Energy (BLE), meshnetworking protocols such as ZigBee or other wireless networkingtechnologies. Many different types of wireless client devices, such aslaptop computers, smartphones, tablets, wearable devices, appliances,and Internet of Things (IoT) devices, incorporate wireless communicationtechnology and can be configured to connect to wireless access pointswhen the device is in range of a compatible wireless access point inorder to access a wired network.

Wireless access networks, and computer networks in general, are complexsystems which may experience transient and/or permanent issues. Some ofthe issues may result in noticeable system performance degradation whileother issues may resolve themselves without substantially affecting thesystem level performance as perceived by the users. Some issues may beexpected and accepted under a heavy load and as soon as the loadsubsides, self-healing mechanisms, such as a retry, etc. may cause theissue to go away. Other self-healing network may involve operations suchas automatically invoking restart of certain components.

SUMMARY

In general, this disclosure describes techniques for performing networkmanagement actions based on a classification of an access point. Anetwork management system (NMS) receives network data associated with aplurality of client devices in a wireless network at a site. The networkdata is indicative of one or more aspects of wireless networkperformance. The NMS may determine a network event associated with thenetwork data. For example, the NMS can determine that the network dataindicates a degradation in performance metrics, and, based on thenetwork data, can determine a root cause for the degradation.Additionally, the NMS can automatically determine appropriate networkmanagement actions to respond to the network event. For example, in thecase where the network event is a degradation in SLE metrics, thenetwork management action may be to perform one or more mitigationactions, or suggest mitigation actions, to remedy the degradation in SLEmetrics. In some cases, a degradation in SLE metrics may be expected orunavoidable, and in such cases, no mitigation actions are necessary. Asan example, client devices associated with an access point that is nearan entrance/exit of a site are expected to experience degraded SLEmetrics as users of the client devices exit the site. Similarly, clientdevices associated with an access point that is near an elevator may beexpected to experience a degradation in SLE metrics as users of theclient devices enter the elevator. APs that are located in areas wheredegradation of SLE metrics may be expected due to users leaving the areamay be referred to as “edge APs” as they are typically at an edge ofgraph representing paths through a wireless network. Such APs may alsobe referred to as “transition APs” as a reflection of the fact thatclient devices often transition in and out of a wireless network at suchan AP.

In some aspects, an NMS may utilize the techniques disclosed herein to,in response to a network event, determine a classification of an AP, andto determine a network management action based on the classification ofthe AP. For example, a network event can be a degradation in SLE metricsexperienced by one or more clients. In the case that the NMS indicatesthat there is a high probability that the root cause of degradation inSLE metrics is due to a fault in an identified AP, the NMS can determineif the identified AP is classified as an edge AP. If the identified APis classified as an edge AP, the NMS can bypass actions to mitigate thefault. If the identified AP is not an edge AP, the NMS can initiateaction to mitigate the fault in the identified AP. In some cases, theaction to mitigate the fault in the identified AP may involve restartingor resetting the AP. This can make the AP unusable for the time it takesto complete the restart or reset. It is thus desirable to avoidmitigation actions that are unnecessary.

In one example, this disclosure describes a system that includes aplurality of access point (AP) devices configured to provide a wirelessnetwork at a site; and a network management system includes a memorystoring network data received from the plurality of AP devices, thenetwork data collected by the plurality of AP devices or a plurality ofclient devices associated with the wireless network, and one or moreprocessors coupled to the memory and configured to: receive a timeseries of SLE metrics, the SLE metrics based on the network data,determine, based on the time series of the SLE metrics, whether anetwork event has occurred, in response to a determination that anetwork event has occurred, determine a root cause for the networkevent, and in response to a determination that the root cause of thenetwork event is associated with an AP device of the plurality of APdevices, determine a classification of the AP device, and determine anetwork management action for the AP device based on the network eventand the classification of the AP device.

In another example, this disclosure describes a network managementsystem (NMS) that manages a plurality of access point (AP) devices in awireless network includes a memory storing network data received fromthe plurality of AP devices, the network data collected by the pluralityof AP devices or a plurality of client devices associated with thewireless network; and one or more processors coupled to the memory andconfigured to: receive a time series of SLE metrics determined from thenetwork data, determine, based on the time series of the SLE metrics,whether a network event has, in response to a determination that anetwork event has occurred, determine a root cause for the networkevent, and in response to a determination that the root cause of thenetwork event is associated with an AP device of the plurality of APdevices, determine a classification of the AP device, and determine anetwork management action for the AP device based on the network eventand the classification of the AP device.

In another example, this disclosure describes a method that includesreceiving, from a plurality of AP devices, network data collected by theplurality of AP devices of a wireless network or a plurality of clientdevices associated with the wireless network; determining a time seriesof SLE metrics associated based on the network data; determining, basedon the time series of the SLE metrics, whether a network event hasoccurred; in response to determining that a network event has occurred,determining a root cause for the network event, and in response todetermining that the root cause of the network event is associated withan AP device of the plurality of AP devices, determining aclassification of the AP device, and determining a network managementaction for the AP device based on the network event and theclassification of the AP device.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example network system in which anetwork management system is configured to classify access points andperform network management actions based on the classification of theaccess point, in accordance with one or more techniques of thedisclosure.

FIG. 2 is a block diagram of an example access point device, inaccordance with one or more techniques of the disclosure.

FIG. 3 is a block diagram of an example network management systemconfigured to classify access points and perform network managementactions based on the classification of the access point, in accordancewith one or more techniques of the disclosure.

FIG. 4 is a conceptual diagram illustrating an example arrangement ofaccess points at a site, in accordance with one or more techniques ofthe disclosure.

FIG. 5 is a conceptual diagram illustrating an example graphrepresenting network device connectivity, in accordance with one or moreaspects of the disclosure.

FIGS. 6A and 6B are conceptual diagrams illustrating examples of inputsand outputs of a VNA/AI engine, in accordance with techniques of thedisclosure.

FIG. 7 is a block diagram of an example user equipment device, inaccordance with one or more techniques of the disclosure.

FIG. 8 is a block diagram of an example network node, such as a routeror switch, in accordance with one or more techniques of the disclosure.

FIG. 9 is a flowchart of example operations performed by a networkmanagement system to classify access points and perform networkmanagement actions based on the classification of the access point, inaccordance with one or more techniques of the disclosure.

FIG. 10 is a flowchart illustrating example operations performed by anetwork management system to determine a classification of an AP.

FIG. 11 is a flowchart illustrating example operations performed by anetwork management system to determine a network management action basedon a classification of an AP.

DETAILED DESCRIPTION

Commercial premises, such as offices, hospitals, airports, stadiums, orretail outlets, often install complex wireless network systems,including a network of wireless access points (APs), throughout thepremises to provide wireless network services to one or more wirelessclient devices. The client devices may include, for example, smartphones or other mobile computing devices, display devices, Internet ofThings (IoT) devices, etc. In cases where there are multiple APs at asite that are available to a client device, the client device mayassociate with the AP that can provide the best (e.g., strongest)signal.

A wireless network service provider may collect network data to monitorwireless network behavior and measure one or more aspects of wirelessnetwork performance at a site. The network data may be collected from,for example, one or more of the client devices and/or one or more of APsassociated with the wireless network. One or more service levelexpectation (SLE) metrics determined based on the collected network datacan be used to measure various aspects of wireless network performance.SLE metrics seek to measure and understand network performance from theviewpoint of the end user experience on the network. One example SLEmetric is a coverage metric, which periodically (e.g., every 5 seconds)measures the signal strength indicator corresponding to the powerreceived by a client from the AP with which the client is associated.Another example SLE metric is a roaming metric, which tracks a clientdevice's percentage of successful roams between two access points. Otherexample SLE metrics may include time to connect, throughput, successfulconnects, capacity, AP health, and/or any other metric that may beindicative of one or more aspects of wireless network performance. TheSLE metrics may also include parameters such as a received signalstrength indicator (RSSI) as measured by the client device, thesignal-to-noise ratio (SNR) of the wireless signal as measured by theclient device, etc. A network service provider may customize andconfigure various thresholds for the SLE metrics to define service levelexpectations at the site. A network management system can generate anetwork event in response to one or more of the thresholds beingcrossed. In response to the network event, the network management systemmay determine a root cause of the network event. For example, in thecase that the network event comprises an error in the operation orconfiguration of the wireless network, the network management system mayautomatically identify the root cause(s) of any SLE metrics that do notsatisfy the thresholds, and may automatically implement one or morenetwork management actions to address the root cause and thus improvethe performance wireless network performance.

The network management actions may be based on a classification of anetwork device such as an AP. Degradation of SLE metrics may beindicative of an error or fault in the configuration or operation of anetwork device or devices, and mitigation actions are typicallyperformed in such cases to resolve the error or fault. However, incertain situations, the degradation of SLE metrics may be expected andsafely ignored for certain classes of APs. For example, as noted above,degradation in SLE metrics can be expected when a user exits a site,enters an elevator, or make some other transition away from an APwithout associating with a different AP at a site. However, currentsystems typically cannot recognize cases where degradation in SLEmetrics is to be expected and ignored. As a result, a network managementsystem may erroneously indicate that there is some fault in the AP thatneeds to be resolved, when in fact, the AP may be operating normally. Anerroneous identification of an AP as being faulty can result inunnecessary costs to a network service provider. For example, resourcesmay be wasted performing automated testing and resetting of the AP.Further, sending a technician to diagnose the AP when in fact there isno problem with the AP is a waste of time and resources. Further,customers may be inconvenienced by the downtime while the AP is beingdiagnosed and reset.

In accordance with one or more techniques of the disclosure, a networkmanagement system can determine that a network event has occurred, suchas degradation in SLE metrics. In some aspects, a network managementsystem can determine a classification for an AP. For example, thenetwork management system may determine whether or not the AP isclassified as an edge AP (also referred to as a “transition AP”). Thenetwork management system may classify the AP as an edge AP in responseto indicators that the AP is on the periphery of an AP neighborhoodgraph representing paths through a wireless network, or other indicatorsthat the AP is located near a location where client devices transitionon and off a wireless network provided by a wireless service provider.Examples of such locations are entrances/exits of buildings, elevators,stairs, APs next to a parking lot, etc. The NMS can determine if the APis classified as an edge AP. The network management system can determinea network management action based on the root cause of the network eventand the classification of the AP. For example, if the AP is classifiedas an edge AP, the NMS can bypass any remediation actions for the AP. Ifthe AP is not an edge AP, the NMS can perform mitigation actions for theAP directed at improving or restoring the operation of the AP, which inturn can improve SLE metrics.

FIG. 1 is a block diagram of an example network system in which anetwork management system (NMS) is configured to classify networkdevices and control the mitigation action based on the classification ofa device that has been identified as a root causes of degraded networkperformance, in accordance with one or more techniques of thedisclosure. Example network system 100 includes a plurality sites102A-102N at which a network service provider manages one or morewireless networks 106A-106N, respectively. Although in FIG. 1 each site102A-102N is shown as including a single wireless network 106A-106N,respectively, in some examples, each site 102A-102N may include multiplewireless networks, and the disclosure is not limited in this respect.

Each site 102A-102N includes a plurality of access points (APs),referred to generally as APs 142. For example, site 102A includes aplurality of APs 142A-1 through 142A-M. Similarly, site 102N includes aplurality of APs 142N-1 through 142N-L. Each AP 142 may be any type ofwireless access point, including, but not limited to, a commercial orenterprise AP, a router, or any other device capable of providingwireless network access.

Each site 102A-102N also includes a plurality of client devices,otherwise known as user equipment devices (UEs), referred to generallyas UEs 148, representing various wireless-enabled devices within eachsite. For example, a plurality of UEs 148A-1 through 148A-J arecurrently located at site 102A. Similarly, a plurality of UEs 148N-1through 148N-K are currently located at site 102N. Each UE 148 may beany type of wireless client device, including, but not limited to, amobile device such as a smart phone, tablet or laptop computer, apersonal digital assistant (PDA), a wireless terminal, a smart watch,smart ring or other wearable device. UEs 148 may also include IoT clientdevices such as printers, security devices, environmental sensors, orany other device configured to communicate over one or more wirelessnetworks.

Example network system 100 also includes various networking componentsfor providing networking services within the wired network including, asexamples, an Authentication, Authorization and Accounting (AAA) server110 for authenticating users and/or UEs 148, a Dynamic HostConfiguration Protocol (DHCP) server 116 for dynamically assigningnetwork addresses (e.g., IP addresses) to UEs 148 upon authentication, aDomain Name System (DNS) server 122 for resolving domain names intonetwork addresses, a plurality of servers 128 (e.g., web servers,databases servers, file servers and the like), and a network managementsystem (NMS) 150.

As shown in FIG. 1 , the various devices and systems of network 100 arecoupled together via one or more network(s) 134, e.g., the Internetand/or an enterprise intranet. Each one of the servers 110, 116, 122,and/or 128, APs 142, UEs 148, NMS 150, and any other servers or devicesattached to or forming part of network system 100 may include a systemlog or an error log module wherein each one of these devices records thestatus of the device including normal operational status and errorconditions.

In the example of FIG. 1 , NMS 150 is a cloud-based computing platformthat manages wireless networks 106A-106N at one or more of sites102A-102N. As further described herein, NMS 150 provides an integratedsuite of management tools and implements various techniques of thedisclosure.

In accordance with the techniques described herein, NMS 150 monitorsnetwork data associated with wireless networks 106A-106N at each site102A-102N, respectively, and manages network resources, such as APs 142at each site, to deliver a high-quality wireless experience to endusers, IoT devices and clients at the site. The network data may bestored in a database associated with NMS 150, such as database 152. Ingeneral, NMS 150 may provide a cloud-based platform for network dataacquisition, monitoring, activity logging, reporting, predictiveanalytics, network anomaly identification, invoking remedial actions,and alert generation.

For example, NMS 150 may include a virtual network assistant (VNA) 160that analyzes network data received from one or more UEs 148 and/or oneor more APs 142 in a wireless network, provides real-time insights andsimplified troubleshooting for IT operations, and automatically takescorrective action or provides recommendations to proactively addresswireless network issues. VNA 160 may, for example, include a networkdata processing platform configured to process a very large number,e.g., hundreds or thousands, of concurrent streams of network data fromsensors and/or agents associated with APs 142 and/or nodes within, orattached to, network 134. For example, VNA 160 of NMS 150 may include anetwork performance engine that automatically determines one or more SLEmetrics for each client device 148 in a wireless network 106. VNA 160may also include an underlying analytics and network erroridentification engine and alerting system. VNA 160 may further providereal-time alerting and reporting to notify administrators of anypredicted events, anomalies, trends, and may perform root cause analysisand automated or assisted error remediation.

In some examples, VNA 160 of NMS 150 may apply machine learningtechniques to identify the root cause of error conditions or poorwireless network performance metrics detected or predicted from thestreams of event data. For example, in some aspects, VNA 160 may utilizea machine learning model 137 that has been trained using eithersupervised or unsupervised machine learning techniques to identify theroot cause of error conditions or poor network performance based onnetwork data. VNA 160 may generate a notification indicative of the rootcause and/or invoke one or more corrective or remedial networkmanagement actions that may be taken to address the root cause of theerror conditions or poor wireless network performance metrics. Thenetwork management action may include mitigation actions if the rootcause may be automatically resolved. For example, VNA 160 can invoke oneor more corrective actions to correct the root cause of the errorcondition or poor wireless network performance metrics, thusautomatically improving the underlying wireless network performancemetrics (e.g., one or more SLE metrics) and also automatically improvingthe user experience.

As an example, VNA 160 may determine that a fault or error in theoperation of a network device such as an AP may be the root cause of theerror conditions or poor wireless performance. Additionally, VNA 160 mayimplement one or more of the techniques described herein to determine aclassification for the AP. VNA 160 may determine a network managementaction based on the root cause and the classification of the AP. If VNA160 determines, based on the classification, that the root cause can beignored, then the network management action may be to bypass mitigationactions.

Example details of these and other operations implemented by the VNA 160and/or NMS 150 are described in U.S. application Ser. No. 14/788,489,filed Jun. 30, 2015, and entitled “Monitoring Wireless Access PointEvents,” U.S. application Ser. No. 16/835,757, filed Mar. 31, 2020, andentitled “Network System Fault Resolution Using a Machine LearningModel,” U.S. application Ser. No. 16/279,243, filed Feb. 19, 2019, andentitled “Systems and Methods for a Virtual Network Assistant,” U.S.application Ser. No. 16/237,677, filed Dec. 31, 2018, and entitled“Methods and Apparatus for Facilitating Fault Detection and/orPredictive Fault Detection,” U.S. application Ser. No. 16/251,942, filedJan. 18, 2019, and entitled “Method for Spatio-Temporal Modeling,” U.S.application Ser. No. 16/296,902, filed Mar. 8, 2019, and entitled“Method for Conveying AP Error Codes Over BLE Advertisements,” and U.S.application Ser. No. 17/303,222, filed May 24, 2021, and entitled,“Virtual Network Assistant Having Proactive Analytics and CorrelationEngine Using Unsupervised ML Model,” all of which are incorporatedherein by reference in their entirety.

In operation, NMS 150 observes, collects and/or receives network data152. The network data is indicative of one or more aspects of wirelessnetwork performance. Network data 152 may take the form of dataextracted from messages, counters and statistics, for example. Thenetwork data may be collected and/or measured by one or more UEs 148and/or one or more APs 142 in a wireless network 106. Some of thenetwork data may be collected and/or measured by other devices in thenetwork system 100. In accordance with one specific implementation, aprocessor or computing device is part of the network management server150. In accordance with other implementations, NMS 150 may comprise oneor more processors, processing circuitry, computing devices, dedicatedservers, virtual machines, containers, services or other forms ofenvironments for performing the techniques described herein. Similarly,computational resources and components implementing VNA 160 may be partof the NMS 150, may execute on other servers or execution environments,or may be distributed to nodes within network 134 (e.g., routers,switches, controllers, gateways and the like).

NMS 150 receives network data associated with a plurality of clientdevices, such as UEs 148, in a wireless network, such as any of wirelessnetwork(s) 106A-106N. The network data is indicative of one or moreaspects of wireless network performance. The network data may bereceived as time series data that is monitored at one or more periodicintervals. The network data may be measured or collected by, forexample, one or more UEs 148 and/or one or more APs 142 associated withthe wireless network 106. Based on the received network data, VNA 160 ofNMS 150 determines one or more SLE metrics associated with each of theplurality of UEs 148 associated with the wireless network 106.

The techniques of the disclosure provide one or more technicaladvantages and practical applications. For example, the techniquesenable the NMS to determine network management actions that may bedirected to particular classifications of APs or other network devices.For example, an NMS can automatically detect degradation in SLE metricsand automatically avoid performance of mitigation actions when thedegradation in SLE metrics are to be expected, for example, when thedegradation in SLE metrics is associated with an AP classified as anedge AP. Such degradation can be expected as part of the normaloperations of the AP as client devices transition in and out of thewireless network. Avoiding or bypassing mitigation actions can beadvantageous in this case because it can avoid unnecessary resourcecosts and customer inconvenience, such as e.g., experiencing SLEdeterioration, associated with performing the unnecessary mitigationactions. As an example, resetting an AP to mitigate a fault can resultin a temporary loss of network connectivity to client devices associatedwith the AP. Further, the temporary loss of connectivity may result inclient devices associating with other APs at the site, which can causean overloading of the other APs. The loss of network connectivity canresult in user dissatisfaction. Further, compute and network resourcescan be wasted when APs are reset unnecessarily. The techniques describedherein can avoid unnecessary mitigation actions, thereby preventingwasteful use of resources and more efficient network operation.

Although the techniques of the present disclosure are described in thisexample as performed by NMS 150, it shall be understood that techniquesdescribed herein may be performed by any other computing device(s),system(s), and/or server(s), and that the disclosure is not limited inthis respect. For example, one or more computing device(s) configured toexecute the functionality of the techniques of the disclosure may residein a dedicated server or be included in any other server (such as any ofservers 128A-128N) in addition to or other than NMS 150, or may bedistributed throughout network 100, and may or may not form a part ofNMS 150.

In addition or alternatively, in some examples, network nodes (e.g.,routers or switches within network 134) and/or access points 142 may beconfigured to locally construct, train, apply and retrain unsupervisedML model(s) 137 based on locally collected SLE metrics to determinewhether the collected network event data should be discarded or whetherthe data represents anomalous behavior that needs to be forwarded to NMS150 for further root cause analysis of VNA 350 (FIG. 3 ) to facilitateidentification and resolution of faults.

FIG. 2 is a block diagram of an example access point (AP) device 200configured in accordance with one or more techniques of the disclosure.Example access point 200 shown in FIG. 2 may be used to implement any ofAPs 142 as shown and described herein with respect to FIG. 1 . Accesspoint 200 may comprise, for example, a Wi-Fi, Bluetooth and/or BluetoothLow Energy (BLE) base station or any other type of wireless accesspoint.

In the example of FIG. 2 , access point 200 includes a wired interface230, wireless interfaces 220A-220B, one or more processor(s) 206, memory212, and a user interface 210, coupled together via a bus 214 over whichthe various elements may exchange data and information. Wired interface230 represents a physical network interface and includes a receiver 232and a transmitter 234 for sending and receiving network communications,e.g., packets. Wired interface 230 couples, either directly orindirectly, access point 200 to network(s) 134 of FIG. 1 . First andsecond wireless interfaces 220A and 220B represent wireless networkinterfaces and include receivers 222A and 222B, respectively, eachincluding a receive antenna via which access point 200 may receivewireless signals from wireless communications devices, such as UEs 148of FIG. 1 . First and second wireless interfaces 220A and 220B furtherinclude transmitters 224A and 224B, respectively, each includingtransmit antennas via which access point 200 may transmit wirelesssignals to wireless communications devices, such as UEs 148 of FIG. 1 .In some examples, first wireless interface 220A may include a Wi-Fi802.11 interface (e.g., 2.4 GHz and/or 5 GHz) and second wirelessinterface 220B may include a Bluetooth interface and/or a Bluetooth LowEnergy (BLE) interface. However, these are given for example purposesonly, and the disclosure is not limited in this respect.

Processor(s) 206 are programmable hardware-based processors configuredto execute software instructions, such as those used to define asoftware or computer program, stored to a computer-readable storagemedium (such as memory 212), such as non-transitory computer-readablemediums including a storage device (e.g., a disk drive, or an opticaldrive) or a memory (such as Flash memory or RAM) or any other type ofvolatile or non-volatile memory, that stores instructions to cause theone or more processors 206 to perform one or more of the techniquesdescribed herein.

Memory 212 includes one or more devices configured to store programmingmodules and/or data associated with operation of access point 200. Forexample, memory 212 may include a computer-readable storage medium, suchas non-transitory computer-readable mediums including a storage device(e.g., a disk drive, or an optical drive) or a memory (such as Flashmemory or RAM) or any other type of volatile or non-volatile memory,that stores instructions to cause the one or more processor(s) 206 toperform one or more of the techniques described herein.

In this example, memory 212 stores executable software including anapplication programming interface (API) 240, a communications manager242, configuration settings 250, a device status log 252 and datastorage 254. Device status log 252 includes a list of network parametersand/or network events specific to access point 200. The networkparameters may include, for example, any network parameter indicative ofone or more aspects of performance of the wireless network. In someexamples, network parameters may include a plurality of states measuredperiodically as time series data that can be translated into one or moreSLE metrics. The network parameters may be measured by the UE devices148, the APs 142/300 or another device associated with the wirelessnetwork.

Network events may include, for example, access point events and/or UEevents. The access point events and/or UE events may each include a logof normal network events, neutral network events, and/or error networkevents. The network events may include, for example, memory status,reboot events, crash events, Ethernet port status, upgrade failureevents, firmware upgrade events, configuration changes, authenticationevents, DNS events, DHCP events, roaming events, etc., as well as a timeand date stamp for each event. Log controller 255 determines a logginglevel for the device based on instructions from NMS 150. Data 254 maystore any data used and/or generated by access point 200, including datacollected from UEs 148, such as data used to calculate one or more SLEmetrics, that is transmitted by access point 200 for cloud-basedmanagement of wireless networks 106A by NMS 150.

Communications manager 242 includes program code that, when executed byprocessor(s) 206, allow access point 200 to communicate with UEs 148,other APs 142, and/or network(s) 134 via any of interface(s) 230 and/or220A-220B. Configuration settings 250 include any device settings foraccess point 200 such as radio settings for each of wirelessinterface(s) 220A-220B. These settings may be configured manually or maybe remotely monitored and managed by NMS 150 to optimize wirelessnetwork performance in real-time, or on a periodic (e.g., hourly ordaily) basis.

Input/output (I/O) 210 represents physical hardware components thatenable interaction with a user, such as buttons, a touchscreen, adisplay and the like. Although not shown, memory 212 typically storesexecutable software for controlling a user interface with respect toinput received via I/O 210.

As described herein, AP device 200 may measure and report network data(i.e., network parameters and/or network event data) from status log 252to NMS 150. The network data is indicative of one or more aspects ofwireless network performance and/or status of the wireless network. Thenetwork data may be measured and/or determined by one or more of the UEdevices and/or by one or more of the APs 200 in a wireless network. APdevice 200 can provide the network data to NMS 150 for use in thetechniques described herein.

FIG. 3 is a block diagram of an example network management systemconfigured to classify access points and perform network managementactions based on the classification of the access point. In someaspects, an NMS can utilize SLE parameters from mobile devices toidentify root causes of degraded network performance. The networkmanagement system can determine whether an access point that isperceived as a root cause device of a fault or error condition is anedge device and based on that can determine whether or not to invoke aremedial action. NMS 300 may be used to implement, for example, NMS 150in FIG. 1 . In such examples, NMS 300 is responsible for monitoring andmanagement of one or more wireless networks 106A-106N at sites102A-102N, respectively. In some examples, NMS 300 receives network datacollected by APs 200 from UEs 148, such as network data used tocalculate one or more SLE metrics, and analyzes this data forcloud-based management of wireless networks 106A-106N. In some examples,NMS 300 may be part of another server shown in FIG. 1 or a part of anyother server.

NMS 300 includes a communications interface 330, one or moreprocessor(s) 306, a user interface 310, a memory 320, and a database312. The various elements are coupled together via a bus 314 over whichthe various elements may exchange data and information.

Processor(s) 306 execute software instructions, such as those used todefine a software or computer program, stored to a computer-readablestorage medium (such as memory 320), such as non-transitorycomputer-readable mediums including a storage device (e.g., a diskdrive, or an optical drive) or a memory (such as Flash memory or RAM) orany other type of volatile or non-volatile memory, that storesinstructions to cause the one or more processors 306 to perform thetechniques described herein.

Communications interface 330 may include, for example, an Ethernetinterface. Communications interface 330 couples NMS 300 to a networkand/or the Internet, such as any of network(s) 134 as shown in FIG. 1 ,and/or any local area networks. Communications interface 330 includes areceiver 332 and a transmitter 334 by which NMS 300 receives/transmitsdata and information to/from any of APs 142, servers 110, 116, 122, 128,and/or any other devices or systems forming part of network 100 such asshown in FIG. 1 . The data and information received by NMS 300 mayinclude, for example, network data and/or event log data received fromAPs 142 used by NMS 300 to remotely monitor and/or control theperformance of wireless networks 106A-106N. NMS may further transmitdata via communications interface 330 to any of network devices such asAPs 142 at any of network sites 102A-102N to remotely manage wirelessnetworks 106A-106N.

Memory 320 includes one or more devices configured to store programmingmodules and/or data associated with operation of NMS 300. For example,memory 320 may include a computer-readable storage medium, such asnon-transitory computer-readable mediums including a storage device(e.g., a disk drive, or an optical drive) or a memory (such as Flashmemory or RAM) or any other type of volatile or non-volatile memory,that stores instructions to cause the one or more processor(s) 306 toperform the techniques described herein.

In this example, memory 320 includes an API 322, a virtual networkassistant (VNA)/AI engine 350, a radio resource management (RRM) engine360, and a network device classifier 370. VNA/AI engine 350 includes anetwork performance engine 352 and a root cause analysis engine 354. NMS300 may also include any other programmed modules, software enginesand/or interfaces configured for remote monitoring and management ofwireless networks 106A-106N, including remote monitoring and managementof any of APs 142/200.

Network performance engine 352 enables set up and tracking of thresholdsfor SLE metrics for each of wireless networks 106A-106N. Networkperformance engine 352 further analyzes network data collected by APsand or UEs associated with wireless networks 106A-106N, such as any ofAPs 142 from UEs 148 in each wireless network 106A-106N. For example,APs 142A-1 through 142A-M collect network data from UEs 148A-1 through148A-J currently associated with wireless network 106A. This data, inaddition to any network data collected by one or more APs 142A-1 through142A-M in wireless network 106A, is transmitted to NMS 300, whichexecutes network performance engine 352 to determine one or more SLEmetrics for each UE 148A-1 through 148A-J associated with wirelessnetwork 106A. One or more of the SLE metrics may further be aggregatedto each AP at a site to gain insight into each APs contribution towireless network performance at the site. The SLE metrics track whetherthe service level meets the configured threshold values for each SLEmetric. If a metric does not meet the configured SLE threshold value forthe site, the failure or degradation may be attributed to one of theclassifiers to further understand how and/or why the failure ordegradation occurred.

Example SLE metrics are shown in below in Table 1.

TABLE 1 Time to Connect The number of connections that took longer thana specified threshold to connect to the internet. Throughput The amountof time, that a client's estimated throughput is below a specifiedthreshold. Coverage The number of user minutes that a client's RSSI asmeasured by the access point is below a specified threshold. CapacityThe number of user minutes that a client experiences ″poor″ capacity.Roaming The percentage of successful roams between 2 access points forclients that are within a specified target time that it takes for aclient to roam. Successful Connects The percentage of successfulAuthorization, Association, DHCP, ARP, and DNS attempts during aninitial connection by a client to the network, when a client roams fromone AP to the next, and on an on-going basis. AP Health This may becalculated based on AP Reboots, AP Unreachable events, and Site Downevents.

RRM engine 360 monitors one or more metrics for each site 106A-106N inorder to learn and optimize the RF environment at each site. Forexample, RRM engine 360 may monitor the coverage and capacity SLEmetrics for a wireless network 106 at a site 102 in order to identifypotential issues with coverage and/or capacity in the wireless network106 and to make adjustments to the radio settings of the access pointsat each site to address the identified issues. For example, RRM engine360 may determine channel and transmit power distribution across all APs142 in each network 106A-106N. For example, RRM engine 360 may monitorevents, power, channel, bandwidth, and number of clients connected toeach AP. RRM engine 360 may further automatically change or updateconfigurations of one or more APs 142 at a site 106 with an aim toimprove the coverage and capacity SLE metrics and thus to provide animproved wireless experience for the user.

NMS 300 includes network device classifier 370 that can determine aclassification for a network device. Network device classifier 370 canapply various heuristics to data from various sources to determine aclassification for a network device. As an example, network deviceclassifier 370 can utilize network data received from network devices onwireless networks 106A-106N, data from servers 110, 116, 122, and128A-128P (FIG. 1 ) among other sources to determine a classificationfor a network device. Various aspects of network device classifier 370are discussed below with reference to an example site illustrated inFIG. 4 .

FIG. 4 is a conceptual diagram illustrating an example arrangement ofaccess points at a site, in accordance with one or more techniques ofthe disclosure. In this example, a site 400 includes APs 402A-402G(referred to generically as “AP 402”). Site 400 may be a floor of abuilding and APs 402A-402G may be, for example, APs arranged on withinsite 400. Dashed lines between the APs 402A-402G indicate that the APscan communicate with one another, or alternatively, receive a radiosignal from a neighboring AP with an RSSI greater than a predeterminedthreshold. In this example, AP 402A is located near an entrance 401 tosite 400, and AP 402D is located near an elevator 406 of site 400. UE424A and UE 424B may be UEs of a customer, employee, or other person atsite 400 or entering/exiting site 400. Also shown in FIG. 4 is networkmanagement system 420, which network management system may be networkmanagement system 150 (FIG. 1 ) or network management system 300 (FIG. 3).

Returning to FIG. 3 , network device classifier 370 may determine aclassification for an AP 402. In some aspects, network classifier 370may determine a classification of an AP according to a betweennesscentrality value for the AP. Betweenness centrality is a measurecentrality of a node in a graph based on shortest paths between nodes.Network classifier 370 can construct a graph of the nodes, where an edgebetween nodes indicates a communication connection between the nodes. Insome aspects, nodes in the graph can represent APs, and edges canindicate that an AP can receive a radio signal from a neighboring APwith an RSSI greater than a predetermined threshold. For every pair ofnodes in the graph, network classifier 370 can determine a shortest pathbetween the nodes that minimizes the number of edges that the pathpasses through. The betweenness centrality value for a node is thenumber of shortest paths that pass through the node. In some aspects,the edges can be weighted based on capacity, cost, etc. In this case,the network device classifier 370 can determine the shortest path as thepath that minimizes the sum of the weights of the paths. Further detailson determination of betweenness centrality can be found in Freeman,Linton. 1977. “A set of measures of centrality based on betweenness.”Sociometry. Vol. 40, No. 1, which is hereby incorporated by referenceherein.

Using the betweenness centrality algorithm, all of the APs in FIG. 4other than AP 402E may be identified as edge devices. While UEsassociated with AP 402A and AP 402D may experience SLE deterioration asa user leaves the site 400 or enters the elevator 406, UEs associatedwith the other edge APs may be actually outside of the enterprisebuilding e.g., in a parking lot, and as such may experience SLEdeterioration as the user of the UE drives away from the site 400.

FIG. 5 is a conceptual diagram illustrating an example graph 500representing network devices and their neighbors, in accordance with oneor more aspects of the disclosure. Two APs can be considered to beneighbors if a first AP receives the radio signal from a second AP at apower level greater than a predetermined threshold. In some aspects, thepower level may be indicated by an RSSI value. Circles in the graph arenodes that represent network devices such as APs in an example network,and edges between the circles indicate that one AP receives a radiosignal from a neighboring AP with an RSSI greater than a predeterminedthreshold. The nodes have been shaded according to the number ofshortest paths passing through the node, where a darker shade indicatesa greater number of shortest paths passes through the node than nodesthat have a lighter shade. In this example, node 502 has the greatestnumber of shortest paths passing through the node. Thus, node 502 may beclassified as a “central” node. Node 504 has the fewest shortest pathspassing through the node and may be classified as an edge node. Nodes506A-506G also have relatively few shortest paths passing through them.Thus, nodes 506A-506G may also be classified as “edge” nodes. Nodes thatare not a central node or an edge node (e.g., nodes 508A-508E) may beclassified as an “interior” node. As will be discussed in further detailbelow, a node that is not classified as an edge node according to abetweenness centrality value may be classified as an edge node usingother heuristics. For example, any of nodes 508A-508E that areclassified as interior nodes according to betweenness centrality may beclassified as an edge node based on other heuristics.

Returning to FIG. 3 and using the example site 400 of FIG. 4 , networkdevice classifier 370 may construct a graph indicating the neighborhoodconnections between APs 402A-402G. In this example, nodes 402A and 402Dmay have the fewest shortest paths through the AP. Network deviceclassifier 370 may thus classify APs 402A and 402D as edge APs. Inaccordance with another example implementation all of the APs other thanAP 402E may be identified as edge APs since users from outside theenterprise structure, e.g., users from a parking lot adjacent to or nearsite 400, may use the wireless network 405 and experience an expectedSLE deterioration as they move/drive away from the site 400.

In some aspects, network device classifier 370 may use variousheuristics to classify a device as an edge device. In accordance withone example implementation network device classifier may use bandwidthmetrics to classify an AP 402 as an edge AP. For instance, bandwidth ofan AP that is close to an entrance of a site is typically used less thanbandwidth of an interior AP. As an example, a site 400 may have officesand conferences rooms that are serviced by interior APs, while areception area at an entrance to the site may be serviced by an edge AP.Client devices in offices or conference rooms are more likely to beinvolved in applications that have higher data volumes than applicationsthat may be used by client devices in the reception area. Additionally,client devices in a reception area are more likely to transition to aninterior AP as the user moves from place to place at the site. Networkdevice classifier 370 may analyze bandwidth usage of UEs associated withan AP 402 and classify, as an edge AP, an AP 402 where UE bandwidthusage is below a threshold value.

Similarly, in some aspects, network device classifier 370 may useconnection time metrics to classify an AP 402 as an edge AP. It istypically the case that connection times of an edge AP are less thanthat of other APs. For instance, connection times of an AP that is closeto an entrance of a site is typically less than connection times ofinterior APs. As with the example above, a site 400 may have offices andconferences rooms that are serviced by interior APs, while a receptionarea at an entrance to the site may be serviced by an edge AP. Clientdevices in offices or conference rooms are more likely to remainconnected to an AP than a client device in a reception area. Clientdevices in a reception area are more likely to transition from the edgeAP to an interior AP as the user moves from place to place at the site.Network device classifier 370 may analyze connection time metrics andclassify an AP 402 where connection times are below a threshold time asan edge AP.

In some aspects, network device classifier 370 may determine aclassification for an AP according to a count of the number of times anAP 402 is the first AP in a wireless network to associate with clientdevices. Again using the example site 400 illustrated in FIG. 4 , userscan enter site 400 via entrance/exit 401. Because AP 402A is the nearestAP to entrance/exit 401, it is likely that AP 402A will be the first APof wireless network 405 that a UE 424A carried by a user entering site400 will associate with. Similarly, in the example illustrated in FIG. 4, AP 402D is the nearest AP to elevator 406. Because AP 402D is thenearest to elevator 406, it is likely that AP 402D will be the first APof wireless network 405 that a UE 424B carried by a user exiting theelevator will associate with. Network device classifier 370 (or othermodule of network management system 420) can maintain a count of thenumber of times an AP 402 is the first AP a client device associateswith. This association will be referred to as a “first association.”

Network device classifier 370 can use the first association value todetermine if an AP 402 should be classified as an edge AP. As anexample, network device 370 may determine a ratio of the number of firstassociations for an AP to the total number of associations for the AP402. If the ratio is above a threshold percentage, network deviceclassifier can classify the AP as an edge AP.

In some aspects, network device classifier 370 may determine aclassification for an AP according to a count of the number of times anAP is the last AP in a wireless network to disassociate with clientdevices. Again using the example site 400 illustrated in FIG. 4 , userscan exit site 400 via entrance/exit 401. Because AP 402A is the nearestAP to entrance/exit 401, AP 402A may be the last AP of wireless network405 that a UE 424A carried by a user exiting site 400 will be associatedwith before transitioning off wireless network 405. Similarly, becauseAP 402D is the nearest to elevator 406, AP 402D may be the last AP ofwireless network 405 that a UE 424B carried by a user entering theelevator will be associated with. Network device classifier 370 (orother module of network management system 420) can maintain a count ofthe number of times an AP 402 is the last AP a client device isassociated with before transitioning off wireless network 405. Thisassociation will be referred to as a “last association.”

Network device classifier 370 can use the last association value todetermine if an AP 402 should be classified as an edge AP. As anexample, network device 370 may determine a ratio of the number of lastassociations for an AP to the total number of associations for the AP402. If the ratio is above a threshold percentage, network deviceclassifier can classify the AP as an edge AP.

In some aspects, network device classifier 370 may determine aclassification for an AP according to a count of the number of times atransition is made from the AP to a different AP on a different floor ofa site. As an example, a user may leave one floor via stairs, escalator,elevator etc. and go to a different floor. A UE 424 carried by a usermay remain connected to wireless network 405, but may disassociate fromthe AP on the original floor and reassociate with an AP on the differentfloor. Network device classifier 370 (or other module of networkmanagement system 420) can maintain a count of the number of times an AP402 is the last AP a client device is associated with before areassociation with an AP on a different floor. Network device classifier370 may determine that an AP is an edge AP if the count of transitionsto an AP on a different floor is comparatively greater for the AP ascompared to other APs on the floor. Similarly, network device classifiermay utilize a count of transitions from APs on a different floor to theAP to determine if the AP is an edge AP.

VNA/AI engine 350 analyzes network data received from APs 142, 200 aswell as its own data to monitor performance of wireless networks106A-106N. For example, VNA/AI engine 350 may identify network eventsthat may occur during the operation of wireless networks 106A-106N. Forexample, VNA/AI engine 350 may detect events such as an undesired orabnormal states in one of wireless networks 106A-106N. VNA/AI engine 350may use root cause analysis module 354 to identify the root cause of anySLE deterioration events and/or undesired or abnormal states. In someexamples, root cause analysis module 354 utilizes artificialintelligence-based techniques to help identify the root cause of anypoor SLE metric(s) at one or more of wireless network 106A-106N. In someexamples, VNA/AI engine 350 may generate and utilize Bayesian statisticsand information theory to determine a network component that may be theroot cause for the degradation in SLE metrics. VNA/AI engine 350 maydetermine a classification for an AP as described above. Depending onthe classification of the AP, VHA/AI engine 350 may automatically invokeone or more corrective network management actions intended to addressthe identified root cause(s) of one or more poor SLE metrics. Forexample, if the AP is not classified as an edge AP, VNA/AI engine mayinvoke corrective actions. Examples of such corrective actions that maybe automatically invoked by VNA/AI engine 350 may include, but are notlimited to, invoking RRM 360 to reboot one or more APs and/oradjust/modify the transmit power of a specific radio in a specific AP,adding SSID configuration to a specific AP, changing channels on an APor a set of APs, etc. The corrective actions may further includerestarting a switch and/or a router, invoke downloading of new softwareto an AP, switch, or router, etc. These corrective actions are given forexample purposes only, and the disclosure is not limited in thisrespect. If automatic corrective actions are not available or do notadequately resolve the root cause, VNA/AI engine 350 may proactively andautomatically provide a notification including recommended correctiveactions to be taken by IT personnel to address the network error.

If the AP is classified as an edge AP, then in some aspects, VNA/AIengine 350 may determine that no corrective action need be performed onthe assumption that the degradation is normal and the result of a UEtransitioning off wireless network 405. In this case, the networkmanagement action is to bypass corrective actions.

FIG. 6A is a conceptual diagram illustrating example inputs and outputsof a VNA/AI engine 350, in accordance with techniques of the disclosure.In the example illustrated in FIG. 6A, VNA/AI engine 350 receives a timeseries 602 of network data. In some aspects, the time series of networkdata 602 includes sets of parameters 604. A set of parameters 604 caninclude SLE metrics, or parameters derived from SLE metrics (denoted asP1-PN in set 604). Additionally, a set of parameters can include anidentifier (ID) of the client device associated with the SLE metrics ofthe corresponding set of parameters 604. In some example implementationthe SLE parameters 602 also include an identifier indicating the AP withwhich the client was associated. Although not shown in FIG. 6 , a set ofparameters 604 can also include a timestamp of when the SLE parameterswere created or collected.

VNA/AI engine 350 can receive the time series 602 of network data and,in response to detection of a network event such as a degradation in SLEmetrics, can analyze the time series 602 of network data to determineroot cause data 606 associated with the degradation in SLE metrics. Insome aspects, root cause data can include, in addition to anidentification of the root cause of the degradation in SLE metrics, acorrective action or actions to remediate the degradation in SLEmetrics. NMS 300, in some aspects, can automatically apply thecorrective actions. Network management system 600 can perform responseheuristics 608 to determine network management actions to remedy, ifnecessary, the root cause of the degradation in SLE metrics.

In some aspects, VNA/AI engine 350 may utilize a machine learning model630 as part of determining root cause data 606. In some aspects, machinelearning model 630 is constructed during a supervised or unsupervisedtraining phase. After such training, parameters P1-PN are supplied inreal time (or near real time) as input to machine learning model 630 todetermine root cause 606. In the example illustrated in FIG. 6A, networkdevice classifier 370 can implement one or more heuristics to classifyan AP that are independent of the heuristics used by machine learningmodel 630 to determine root cause data 606.

Network management system 600 can determine a classification of an APthat is associated with the degradation in SLE metrics from networkdevice classifier 370 described above. If the AP is classified as anedge AP, response heuristics 608 may determine that the networkmanagement action 610 is to bypass any corrective actions recommended inroot cause data 606. Alternatively, response heuristics 608 maydetermine a network management action 610 that is an alternative to anycorrective actions recommended in root cause data 606. For example,response heuristics 608 may determine that the network management actionis to recommend placement of additional APs near the AP whose clientdevices have experienced degraded SLE metrics. As noted above, thenetwork management system 600 can be configured to provide networkmanagement actions 610 that are based on a client configuration, and maybe different from client to client.

In some aspects, network management system 600 can be configured toprovide network management actions 610 that are based on a specific siteconfiguration, and may be different from one specific site to anotherspecific site. For example, some site operators may configure networkmanagement system 600 such that the network management action for APsclassified as edge APs is to bypass some or all of the correctiveactions recommend by VNA/AI engine 350. Other site operators mayconfigure network management system 600 such that the network managementaction is to alert an IT technician about the network event. recommendplacement of additional APs near the location of the identified edge AP.For example, it may be considered advantageous by a site operator tofacilitate smooth transitions on and off wireless network 405. Adegradation in SLE metrics may result in transitions that are notsmooth. Thus, in the case of a degradation of SLE metrics associate withan edge AP, VNA/AI engine 350 may recommend placement of additional APs.

FIG. 6B is a conceptual diagram illustrating another set of exampleinputs and outputs of a VNA/AI engine 350, in accordance with techniquesof the disclosure. In the example illustrated in FIG. 6B, VNA/AI engine350 receives a time series 622 of network data. In some aspects, thetime series of network data 622 includes sets of parameters 624, which,in addition to the parameters 604 discussed above with respect to FIG.6A, can include device classification 626. Device classification 626 canspecify the classification of the device that was the source of theparameters P1-PN.

VNA/AI engine 350 can receive the time series 622 of network data and,in response to a network event such as a degradation in SLE metrics, cananalyze the time series 622 of network data to determine root cause data606 associated with the degradation in SLE metrics. In some aspects,root cause data can include, in addition to an identification of theroot cause of the degradation in SLE metrics, a corrective action oractions to remediate the degradation in SLE metrics. NMS 300, in someaspects, can automatically apply the corrective actions.

In some aspects, VNA/AI engine 350 may utilize a machine learning model630 as part of determining root cause data 606. In some aspects, machinelearning model 630 is constructed during a supervised or unsupervisedtraining phase. Training data used to construct machine learning model630 can include parameters P1-PN and device classification data. Aftersuch training, parameters P1-PN and device classification data can besupplied in real time (or near real time) as input to machine learningmodel 630 to determine root cause data 606. Network management system600 can use the root cause data to determine network management actions610.

Network management system 600 can determine a classification of an APthat is associated with the degradation in SLE metrics from networkdevice classifier 370 described above. If the AP is classified as anedge AP, response heuristics 608 may determine that the networkmanagement action 610 is to bypass any corrective actions recommended inroot cause data 606. Alternatively, response heuristics 608 maydetermine a network management action 610 that is an alternative to anycorrective actions recommended in root cause data 606. For example,response heuristics 608 may determine that the network management actionis to alert a technician. Yet other heuristics may recommend placementof additional APs near the AP whose client devices have experienceddegraded SLE metrics. As noted above, the network management system 600can be configured to provide network management actions 610 that arebased on a client configuration, and may be different from client toclient.

FIG. 7 shows an example user equipment (UE) device 700. Example UEdevice 700 shown in FIG. 7 may be used to implement any of UEs 148 asshown and described herein with respect to FIG. 1 . UE device 700 mayinclude any type of wireless client device, and the disclosure is notlimited in this respect. For example, UE device 700 may include a mobiledevice such as a smart phone, tablet or laptop computer, a personaldigital assistant (PDA), a wireless terminal, a smart watch, a smartring or any other type of mobile or wearable device. UE 700 may alsoinclude any type of IoT client device such as a printer, a securitysensor or device, an environmental sensor, or any other connected deviceconfigured to communicate over one or more wireless networks.

In accordance with one or more techniques of the disclosure, networkdata indicative of one or more aspects of the performance of a wirelessnetwork (that is, data used by NMS 150 to calculate one or more SLEmetrics) is received from each UE 700 in a wireless network. Forexample, NMS 150 receives network data from UEs 148 in networks106A-106N of FIG. 1 . In some examples, NMS 150 receives relevantnetwork data from UEs 148 on a continuous basis (e.g., every 2 secondsor other appropriate time period), and NMS may calculate one or more SLEmetrics for each UE on a periodic basis as defined by a firstpredetermined period of time (e.g., every 10 minutes or otherpredetermined time period).

UE device 700 includes a wired interface 730, wireless interfaces720A-720C, one or more processor(s) 706, memory 712, and a userinterface 710. The various elements are coupled together via a bus 714over which the various elements may exchange data and information. Wiredinterface 730 includes a receiver 732 and a transmitter 734. Wiredinterface 730 may be used, if desired, to couple UE 700 to network(s)134 of FIG. 1 . First, second and third wireless interfaces 720A, 720B,and 720C include receivers 722A, 722B, and 722C, respectively, eachincluding a receive antenna via which UE 700 may receive wirelesssignals from wireless communications devices, such as APs 142 of FIG. 1, AP 200 of FIG. 2 , other UEs 148, or other devices configured forwireless communication. First, second, and third wireless interfaces720A, 720B, and 720C further include transmitters 724A, 724B, and 724C,respectively, each including transmit antennas via which UE 700 maytransmit wireless signals to wireless communications devices, such asAPs 142 of FIG. 1 , AP 200 of FIG. 2 , other UEs 148 and/or otherdevices configured for wireless communication. In some examples, firstwireless interface 720A may include a Wi-Fi 802.11 interface (e.g., 2.4GHz and/or 5 GHz) and second wireless interface 720B may include aBluetooth interface and/or a Bluetooth Low Energy interface. Thirdwireless interface 720C may include, for example, a cellular interfacethrough which UE device 700 may connect to a cellular network.

Processor(s) 706 execute software instructions, such as those used todefine a software or computer program, stored to a computer-readablestorage medium (such as memory 712), such as non-transitorycomputer-readable mediums including a storage device (e.g., a diskdrive, or an optical drive) or a memory (such as Flash memory or RAM) orany other type of volatile or non-volatile memory, that storesinstructions to cause the one or more processors 706 to perform thetechniques described herein.

Memory 712 includes one or more devices configured to store programmingmodules and/or data associated with operation of UE 700. For example,memory 712 may include a computer-readable storage medium, such asnon-transitory computer-readable mediums including a storage device(e.g., a disk drive, or an optical drive) or a memory (such as Flashmemory or RAM) or any other type of volatile or non-volatile memory,that stores instructions to cause the one or more processor(s) 706 toperform the techniques described herein.

In this example, memory 712 includes an operating system 740,applications 742, a communications module 744, configuration settings750, and data storage 754. Data storage 754 may include, for example, astatus/error log including network data specific to UE 700. The networkdata may include event data such as a log of normal events and errorevents according to a logging level based on instructions from thenetwork management system (e.g., NMS 150/300). Data storage 754 maystore any data used and/or generated by UE 700, such as network dataused to calculate one or more SLE metrics that is collected by UE 700and transmitted to any of APs 142 in a wireless network 106 for furthertransmission to NMS 150.

Communications module 744 includes program code that, when executed byprocessor(s) 706, enables UE 700 to communicate using any of wiredinterface(s) 730, wireless interfaces 720A-720B and/or cellularinterface 750C. Configuration settings 750 include any device settingsfor UE 700 settings for each of wireless interface(s) 720A-720B and/orcellular interface 720C. Configuration settings 750 may also includedevice settings for UE 700 wired network interfaces, when suchinterfaces are present on UE 700.

FIG. 8 is a block diagram illustrating an example network node (orserver) 800 configured according to the techniques described herein. Inone or more examples, the network node 800 implements a device or aserver attached to the network 134 of FIG. 1 , e.g., router, switch, AAAserver, DHCP server, DNS server, VNA, Web server, etc., or a networkdevice such as, e.g., routers, switches or the like. In someembodiments, network node 800 of FIG. 8 is server 110, 116, 122, 128, ofFIG. 1 or routers/switches of network 134 of FIG. 1 .

In this example, network node 800 includes a communications interface802, e.g., an Ethernet interface, a processor 806, input/output 808,e.g., display, buttons, keyboard, keypad, touch screen, mouse, etc., amemory 812 and an assembly of components 816, e.g., assembly of hardwaremodule, e.g., assembly of circuits, coupled together via a bus 809 overwhich the various elements may interchange data and information.Communications interface 802 couples the network node 800 to a network,such as an enterprise network.

Though only one interface is shown by way of example, those skilled inthe art should recognize that network nodes may, and usually do, havemultiple communication interfaces. Communications interface 802 includesa receiver 820 via which the network node 800, e.g. a server, canreceive data and information, e.g., including operation relatedinformation, e.g., registration request, AAA services, DHCP requests,Simple Notification Service (SNS) look-ups, and Web page requests.Communications interface 802 includes a transmitter 822, via which thenetwork node 800, e.g., a server, can send data and information, e.g.,including configuration information, authentication information, webpage data, etc.

Memory 812 stores executable software applications 832, operating system840 and data/information 830. Data 830 includes system log and/or errorlog that stores network data and/or SLE metrics for node 800 and/orother devices, such as wireless access points, based on a logging levelaccording to instructions from the network management system.

FIG. 9 is a flowchart (900) of example operations performed by a networkmanagement system to determine a network management action based on aclassification of an AP. The operations may be performed by a networkmanagement system, such as NMS 150, 300 and/or 600 as shown in FIGS. 1,3, 6A and 6B.

One or more processor(s), such as one or more processor(s) 306 of NMS300, receives network data associated with a plurality of client devicesin a wireless network (902). The client devices may include, forexample, any of UEs 148, and the wireless network may include, forexample, wireless network 106. The network data may be received, forexample, as time series data monitored at one or more periodicintervals. The network data may be measured or collected by, forexample, one or more client devices and/or one or more APs associatedwith the wireless network, and transmitted to the NMS via network(s)134.

The one or more processor(s) determine a time series of SLE metricsbased on the received network data (904). The network data may includethe SLE metrics or data used to derive SLE metrics, identifiers for theclient devices for which data was collected (e.g., UEs), and the APswith which the client devices were associated. For example, the one ormore processor(s) may execute a virtual network assistant 350 or otherheuristics to determine a root cause of SLE degradation based on any oneor more of a time to connect metric, a throughput metric, a coveragemetric, a capacity metric, a roaming metric, a successful connectsmetric, and/or an AP health metric. In addition or alternatively, thismay include determining root cause of SLE deterioration based on areceived signal strength indicator (RSSI) and/or a signal-to-noise (SNR)of the wireless signal received from a current AP to which the clientdevice is associated.

The one or more processors may determine if a network event has occurred(905). As an example, the one or more processors may determine, based onthe time series of SLE metrics, that there has been a degradation in theSLE metrics.

If a network event is not detected based on the time series of SLEmetrics (“NO” branch of 905), the network management system can continueto receive and monitor SLE metrics (902). If a network event is detected(“YES” branch of 905), the one or more processor(s) further determine aroot cause for the network event based on the time series of SLE metrics(906).

The network management system can determine if the root cause isassociated with an AP (908). For example, the one or more processor(s)may determine that there has been an issue with a specific AP thatcaused degradation in SLE metrics for the UEs. If the root cause isassociated with an AP (“YES” branch of 908), then the one or moreprocessors may determine a classification of the AP (910). For example,the classification may be based on betweenness centrality of the AP, orheuristics such as connection times of client devices of the AP,bandwidth utilization of the AP, or any of the techniques describedherein. Further details on techniques for determining whether an AP isan edge AP are discussed below with respect to FIG. 11 .

The one or more processors may apply machine learning techniques andother heuristics to the SLE metrics and the classification of the APdevice to determine a network management action based on the networkevent and the classification of the AP (912). The network managementsystem can continue to receive further network data from client devicesin the wireless network (902).

If the network management system determines that the root cause is notassociated with an AP (“NO” branch of 908), the network managementsystem may perform mitigation actions to remediate the root cause of thenetwork event. As an example, a network event may be associated with aswitch, router, or other network device other than an AP. The networkmanagement system can perform mitigation actions with respect to theswitch, router, or other network device. The network management systemcan continue to receive further network data from client devices in thewireless network (902).

FIG. 10 is a flowchart (1000) illustrating example operations performedby a network management system to determine a classification of an AP.The operations may be performed by a network management system, such asNMS 150, 300 and/or 600 as shown in FIGS. 1, 3, 6A and 6B.

One or more processor(s), such as one or more processor(s) 306 of NMS300, receives network data from each AP 142 indicative of the measuredstrength of signal they receive from other APs 142 (1010).

The NMS can construct a network neighborhood graph wherein each noderepresents an AP and each edge represents an RSSI signal originatingfrom a transmission by a first AP which was received by a second AP withan RSSI greater than a predetermined threshold (1014).

The NMS can perform an initialization process which includes selectionof an initial AP from a list that includes all of the APs (1016). NMS300 can utilize the neighborhood graph to determine the betweennesscentrality of the AP (1018). The betweenness algorithm is one example ofa method for determining whether an AP resides in the center or at theperiphery of the neighborhood graph used in some implementations. Otherimplementations may use different methods for determining whether an APresides in the center or at the periphery of the neighborhood graph.

The NMS can determine, for each AP in the wireless network, a normalizednumber of times a UE joined the wireless network using the AP during apredetermined time period, e.g., in the last week (1022). Similarly, theNMS can determine, for each AP in the wireless network, the normalizednumber of times a UE which was associated with the AP left the wirelessnetwork during a predetermined time period, e.g., in the last week(1024). In accordance with one example implementation the normalizationoperation may include dividing the number of times a UE joined (or left)the network by the number of UEs which were associated with the wirelessnetwork during the abovementioned time period.

The NMS can determine, for each AP in the wireless network, a measure ofa cumulative number of packets transported via the AP during apredetermined time period (1028). In accordance with one exampleimplementation the measure is the number of packets through the specificAP divided by the number of packets transmitted via the wireless networkduring a predetermined time period.

The flowchart 1000 indicate examples of measurement of four parameterssuch as a measure of the betweenness centrality, a measure of the numberof UEs entering the network via the AP, a measure of the number of UEsexiting the network via the AP, and a measure of the load of the networkvia the AP. Other measures may be used for determining an edge APclassification instead of, or in addition to those discussed above.

The NMS can analyze the abovementioned parameters either separately orin aggregation and determine whether the AP should be classified as anedge AP (1032). For example, the NMS can compare each one of theabovementioned parameters to a specific predetermined parameter anddetermines that the AP is an edge AP if any specific parameter meets apredetermined criterion. In another example implementation, the NMS candetermine an aggregated measure of the abovementioned parameters anddetermine that the AP is classified as an edge AP if the aggregatedmeasure of the abovementioned parameters meets a predeterminedcriterion.

The NMS can determine whether in operation 1032 the AP was determined tobe an edge AP (1036). If the AP is classified as an edge AP (“YES”branch of 1036) the NMS indicates the AP is classified as an edge AP.For example, the NMS can mark or tag the AP as an edge AP in a devicedatabase. If the NMS determines that the AP is not classified as an edgeAP (“NO” branch of 1036, the AP is not marked or tagged as an edge AP.In some aspects, the AP may be marked or tagged as an interior AP.

After determining a classification for an AP as described above, the NMScan determine whether there are further APs that need to be examined todetermine whether they are an edge device (1044). If so (“YES” branch or1044), then the next AP to be examined is selected (1048) and the NMSreturns to operation 1018. The NMS can repeat the above describedprocess until all of the APs are marked accordingly as either being anedge device or not an edge device.

In accordance with one implementation once all of the APs weredetermined to be either an edge device or not an edge device, the methodends. In according to another example implementation, the NMS canperiodically reexamine and determine AP classification of APs, ordetermine classification of new APs added to the wireless network.

In accordance with another example implementation, the NMS canperiodically perform the operations shown in flowchart 1000 torecalculate the neighborhood graph to ensure that changes in thetransmission power that may have been performed in compliance withinstructions from the radio resource manager (RRM 360, FIG. 3 ) areproperly reflected in the neighborhood graph and consequently in thebetweenness centricity of each AP.

FIG. 11 is a flowchart (1100) illustrating example operations performedby a network management system to determine a network management actionbased on a classification of an AP. The operations may be performed by anetwork management system, such as NMS 150, 300 and/or 600 as shown inFIGS. 1, 3, 6A and 6B.

One or more processor(s), such as one or more processor(s) 306 of NMS300, receives network data associated with a plurality of client devicesin a wireless network (1102). The client devices may include, forexample, any of UEs 148, and the wireless network may include, forexample, wireless network 106. The network data may be received, forexample, as time series data monitored at one or more periodicintervals. The network data may be measured or collected by, forexample, one or more client devices and/or one or more APs associatedwith the wireless network, and transmitted to the NMS via network(s)134.

The one or more processor(s) determine a time series of SLE metricsbased on the received network data (1104). The network data may includethe SLE metrics or data used to derive SLE metrics, identifiers for theclient devices for which data was collected (e.g., UEs), and the APswith which the client devices were associated.

The one or more processors can examine the measured SLE metric(s) andcompare the measured SLE metric(s) against a threshold that may bedefined by the limit of normal/acceptable SLE measurement (1108). If theone or more processors determine that the network provides an acceptableSLE (e.g., the Wi-Fi network works as designed) (“NO” branch of 1108),the one or more processors can continue to monitor the network operation(1102). However if the one or more processors determine that the SLEmetric(s) indicate that the network performance has deteriorated below apredetermined criterion (“YES” branch of 1108), a VNA/AI of the NMS canuse the measured SLE parameters to determine and identify the root causeof the SLE deterioration (1112).

The NMS can examine whether the identified root cause of the SLEdeterioration is an edge AP (1116). The process of determining whetheran AP is an edge device has been described in greater detail above withrespect to the flowchart of FIG. 10 .

If the NMS determines that the offending device is not an edge AP (“NO”branch of 1116), the NMS proceeds with a normal recovery process basedon a first VNA/AI recommended mitigation action (1120). However if theNMS determines that the offending device is an edge AP (“YES” branch of1116), the NMS proceeds with a second mitigation action which could beless intrusive than the first mitigation action (1124). For example thesecond mitigation action may be just a notification to a technician, anupdate of statistics, or just ignoring the SLE deterioration altogether(e.g., bypassing any mitigation actions).

The NMS can continue to monitor the operation of the system by repeatingthe operations of flowchart 1100 beginning with collecting and examiningadditional time series SLE metric(s) (1102).

The discussion above has been generally presented in the context of anNMS that bypasses mitigation action for a root cause when an AP isclassified as an edge AP. The techniques disclosed herein apply as wellto the case wherein AP devices are classified as centric or interior APdevices (rather than edge devices) and a different mitigation action istaken for centric or interior devices than the actions taken fornon-centric or non-interior AP devices. Further, the techniquesdisclosed herein may be used to determine edge, interior and/or centricclassifications of other network devices such as switches, routers etc.and to determine a network management action based on suchclassification.

The techniques described herein may be implemented using software,hardware and/or a combination of software and hardware. Various examplesare directed to apparatus, e.g., mobile nodes, mobile wirelessterminals, base stations, e.g., access points, communications system.Various examples are also directed to methods, e.g., method ofcontrolling and/or operating a communications device, e.g., wirelessterminals (UEs), base stations, control nodes, access points and/orcommunications systems. Various examples are also directed tonon-transitory machine, e.g., computer readable medium, e.g., ROM, RAM,CDs, hard discs, etc., which include machine readable instructions forcontrolling a machine to implement one or more operations of a method.

It is understood that the specific order or hierarchy of operations inthe processes disclosed is an example approach. Based upon designpreferences, it is understood that the specific order or hierarchy ofoperations in the processes may be rearranged while remaining within thescope of the present disclosure. The accompanying method claims presentelements of the various operations in a sample order and are not meantto be limited to the specific order or hierarchy presented.

In various examples devices and nodes described herein are implementedusing one or more modules to perform the operations corresponding to oneor more methods, for example, signal generation, transmitting,processing, and/or receiving operations. Thus, in some examples variousfeatures are implemented using modules. Such modules may be implementedusing software, hardware or a combination of software and hardware. Insome examples each module is implemented as an individual circuit withthe device or system including a separate circuit for implementing thefunction corresponding to each described module. Many of the abovedescribed methods or method operations can be implemented using machineexecutable instructions, such as software, included in a machinereadable medium such as a memory device, e.g., RAM, floppy disk, etc. tocontrol a machine, e.g., general purpose computer with or withoutadditional hardware, to implement all or portions of the above describedmethods, e.g., in one or more nodes. Accordingly, among other things,various examples are directed to a machine-readable medium e.g., anon-transitory computer readable medium, including machine executableinstructions for causing a machine, e.g., processor and associatedhardware, to perform one or more of the operations of theabove-described method(s). Some examples are directed to a deviceincluding a processor configured to implement one, multiple, or all ofthe operations of one or more methods of the one example aspect.

In some examples, the processor or processors, e.g., CPUs, of one ormore devices, e.g., communications devices such as wireless terminals(UEs), and/or access nodes, are configured to perform the operations ofthe methods described as being performed by the devices. Theconfiguration of the processor may be achieved by using one or moremodules, e.g., software modules, to control processor configurationand/or by including hardware in the processor, e.g., hardware modules,to perform the recited operations and/or control processorconfiguration. Accordingly, some but not all examples are directed to acommunications device, e.g., user equipment, with a processor whichincludes a module corresponding to each of the operations of the variousdescribed methods performed by the device in which the processor isincluded. In some but not all examples a communications device includesa module corresponding to each of the operations of the variousdescribed methods performed by the device in which the processor isincluded. The modules may be implemented purely in hardware, e.g., ascircuits, or may be implemented using software and/or hardware or acombination of software and hardware.

Some examples are directed to a computer program product comprising acomputer-readable medium comprising code for causing a computer, ormultiple computers, to implement various functions, operations, actsand/or steps, e.g. one or more operations described above. In someexamples, the computer program product can, and sometimes does, includedifferent code for each operation to be performed. Thus, the computerprogram product may, and sometimes does, include code for eachindividual operation of a method, e.g., a method of operating acommunications device, e.g., a wireless terminal or node. The code maybe in the form of machine, e.g., computer, executable instructionsstored on a computer-readable medium such as a RAM (Random AccessMemory), ROM (Read Only Memory) or other type of storage device. Inaddition to being directed to a computer program product, some examplesare directed to a processor configured to implement one or more of thevarious functions, steps, acts and/or operations of one or more methodsdescribed above. Accordingly, some examples are directed to a processor,e.g., CPU, graphical processing unit (GPU), digital signal processing(DSP) unit, etc., configured to implement some or all of the operationsof the methods described herein. The processor may be for use in, e.g.,a communications device or other device described in the presentapplication.

Numerous additional variations on the methods and apparatus of thevarious examples described above will be apparent to those skilled inthe art in view of the above description. Such variations are to beconsidered within the scope of this disclosure. The methods andapparatus may be, and in various examples are, used with BLE, LTE, CDMA,orthogonal frequency division multiplexing (OFDM), and/or various othertypes of communications techniques which may be used to provide wirelesscommunications links between access nodes and mobile nodes. In someexamples the access nodes are implemented as base stations whichestablish communications links with user equipment devices, e.g., mobilenodes, using OFDM and/or CDMA. In various examples the mobile nodes areimplemented as notebook computers, personal data assistants (PDAs), orother portable devices including receiver/transmitter circuits and logicand/or routines, for implementing the methods.

In the detailed description, numerous specific details are set forth inorder to provide a thorough understanding of some examples. However, itwill be understood by persons of ordinary skill in the art that someexamples may be practiced without these specific details. In otherinstances, well-known methods, procedures, components, units and/orcircuits have not been described in detail so as not to obscure thediscussion.

Some examples may be used in conjunction with various devices andsystems, for example, a User Equipment (UE), a Mobile Device (MD), awireless station (STA), a wireless terminal (WT), a Personal Computer(PC), a desktop computer, a mobile computer, a laptop computer, anotebook computer, a tablet computer, a server computer, a handheldcomputer, a handheld device, a Personal Digital Assistant (PDA) device,a handheld PDA device, an on-board device, an off-board device, a hybriddevice, a vehicular device, a non-vehicular device, a mobile or portabledevice, a consumer device, a non-mobile or non-portable device, awireless communication station, a wireless communication device, awireless Access Point (AP), a wired or wireless router, a wired orwireless modem, a video device, an audio device, an audio-video (A/V)device, a wired or wireless network, a wireless area network, a WirelessVideo Area Network (WVAN), a Local Area Network (LAN), a Wireless LAN(WLAN), a Personal Area Network (PAN), a Wireless PAN (WPAN), and thelike.

Some examples may be used in conjunction with devices and/or networksoperating in accordance with existing Wireless-Gigabit-Alliance (WGA)specifications (Wireless Gigabit Alliance, Inc. WiGig MAC and PHYSpecification Version 1.1, April 2011, Final specification) and/orfuture versions and/or derivatives thereof, devices and/or networksoperating in accordance with existing IEEE 802.11 standards (IEEE802.11-2012, IEEE Standard for Information technology-Telecommunicationsand information exchange between systems Local and metropolitan areanetworks-Specific requirements Part 11: Wireless LAN Medium AccessControl (MAC) and Physical Layer (PHY) Specifications, Mar. 29, 2012;IEEE802.11ac-2013 (“IEEE P802.11ac-2013, IEEE Standard for InformationTechnology—Telecommunications and Information Exchange BetweenSystems—Local and Metropolitan Area Networks—Specific Requirements—Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications—Amendment 7: Enhancements for Very High Throughput forOperation in Bands below 6 GHz”, December, 2013); IEEE 802.11 ad (“IEEEP802.11 ad-2012, IEEE Standard for InformationTechnology—Telecommunications and Information Exchange BetweenSystems—Local and Metropolitan Area Networks—Specific Requirements—Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications—Amendment 3: Enhancements for Very High Throughput in the60 GHz Band”, 28 Dec. 2012); IEEE-802.11REVmc (“IEEE802.11-REVmcTM/D3.0, June 2014 draft standard for Informationtechnology—Telecommunications and information exchange between systemsLocal and metropolitan area networks Specific requirements; Part 11:Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specification”); IEEE802.11-ay (P802.11 ay Standard for InformationTechnology-Telecommunications and Information Exchange Between SystemsLocal and Metropolitan Area Networks-Specific Requirements Part 11:Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications-Amendment: Enhanced Throughput for Operation inLicense-Exempt Bands Above 75 GHz)), IEEE 802.11-2016 and/or futureversions and/or derivatives thereof, devices and/or networks operatingin accordance with existing Wireless Fidelity (Wi-Fi) Alliance (WFA)Peer-to-Peer (P2P) specifications (Wi-Fi P2P technical specification,version 1.5, August 2014) and/or future versions and/or derivativesthereof, devices and/or networks operating in accordance with existingcellular specifications and/or protocols, e.g., 3rd GenerationPartnership Project (3GPP), 3GPP Long Term Evolution (LTE) and/or futureversions and/or derivatives thereof, units and/or devices which are partof the above networks, or operate using any one or more of the aboveprotocols, and the like.

Some examples may be used in conjunction with one way and/or two-wayradio communication systems, cellular radio-telephone communicationsystems, a mobile phone, a cellular telephone, a wireless telephone, aPersonal Communication Systems (PCS) device, a PDA device whichincorporates a wireless communication device, a mobile or portableGlobal Positioning System (GPS) device, a device which incorporates aGPS receiver or transceiver or chip, a device which incorporates an RFIDelement or chip, a Multiple Input Multiple Output (MIMO) transceiver ordevice, a Single Input Multiple Output (SIMO) transceiver or device, aMultiple Input Single Output (MISO) transceiver or device, a devicehaving one or more internal antennas and/or external antennas, DigitalVideo Broadcast (DVB) devices or systems, multi-standard radio devicesor systems, a wired or wireless handheld device, e.g., a Smartphone, aWireless Application Protocol (WAP) device, or the like.

Some examples may be used in conjunction with one or more types ofwireless communication signals and/or systems, for example, RadioFrequency (RF), Infra-Red (IR), Frequency-Division Multiplexing (FDM),Orthogonal FDM (OFDM), Orthogonal Frequency-Division Multiple Access(OFDMA), FDM Time-Division Multiplexing (TDM), Time-Division MultipleAccess (TDMA), Multi-User MIMO (MU-MIMO), Spatial Division MultipleAccess (SDMA), Extended TDMA (E-TDMA), General Packet Radio Service(GPRS), extended GPRS, Code-Division Multiple Access (CDMA), WidebandCDMA (WCDMA), CDMA 2000, single-carrier CDMA, multi-carrier CDMA,Multi-Carrier Modulation (MDM), Discrete Multi-Tone (DMT), Bluetooth,Global Positioning System (GPS), Wi-Fi, Wi-Max, ZigBee™, Ultra-Wideband(UWB), Global System for Mobile communication (GSM), 2G, 2.5G, 3G, 3.5G,7G, Fifth Generation (5G), or Sixth Generation (6G) mobile networks,3GPP, Long Term Evolution (LTE), LTE advanced, Enhanced Data rates forGSM Evolution (EDGE), or the like. Other examples may be used in variousother devices, systems and/or networks.

Some demonstrative examples may be used in conjunction with a WLAN(Wireless Local Area Network), e.g., a Wi-Fi network. Other examples maybe used in conjunction with any other suitable wireless communicationnetwork, for example, a wireless area network, a “piconet”, a WPAN, aWVAN, and the like.

Some examples may be used in conjunction with a wireless communicationnetwork communicating over a frequency band of 2.4 Ghz, 5 GHz and/or 60GHz. However, other examples may be implemented utilizing any othersuitable wireless communication frequency band(s), for example, anExtremely High Frequency (EHF) band (the millimeter wave (mmWave)frequency band), e.g., a frequency band within the frequency band ofbetween 20 GhH and 300 GHz, a WLAN frequency band, a WPAN frequencyband, a frequency band according to the WGA specification, and the like.

While the above provides just some simple examples of the various deviceconfigurations, it is to be appreciated that numerous variations andpermutations are possible. Moreover, the technology is not limited toany specific channels, but is generally applicable to any frequencyrange(s)/channel(s). Moreover, and as discussed, the technology may beuseful in the unlicensed spectrum.

Although examples are not limited in this regard, discussions utilizingterms such as, for example, “processing,” “computing,” “calculating,”“determining,” “establishing”, “analyzing”, “checking”, or the like, mayrefer to operation(s) and/or process(es) of a computer, a computingplatform, a computing system, a communication system or subsystem, orother electronic computing device, that manipulate and/or transform datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information storage medium that may storeinstructions to perform operations and/or processes.

Although examples are not limited in this regard, the terms “plurality”and “a plurality” as used herein may include, for example, “multiple” or“two or more.” The terms “plurality” or “a plurality” may be usedthroughout the specification to describe two or more components,devices, elements, units, parameters, circuits, or the like. Forexample, “a plurality of stations” may include two or more stations.

It may be advantageous to set forth definitions of certain words andphrases used throughout this document: the terms “include” and“comprise,” as well as derivatives thereof, mean inclusion withoutlimitation; the term “or,” is inclusive, meaning and/or; the phrases“associated with” and “associated therewith,” as well as derivativesthereof, may mean to include, be included within, interconnect with,interconnected with, contain, be contained within, connect to or with,couple to or with, be communicable with, cooperate with, interleave,juxtapose, be proximate to, be bound to or with, have, have a propertyof, or the like; and the term “controller” means any device, system orpart thereof that controls at least one operation, such a device may beimplemented in hardware, circuitry, firmware or software, or somecombination of at least two of the same. It should be noted that thefunctionality associated with any particular controller may becentralized or distributed, whether locally or remotely. Definitions forcertain words and phrases are provided throughout this document andthose of ordinary skill in the art should understand that in many, ifnot most instances, such definitions apply to prior, as well as futureuses of such defined words and phrases.

The examples have been described in relation to communications systems,as well as protocols, techniques, means and methods for performingcommunications, such as in a wireless network, or in general in anycommunications network operating using any communications protocol(s).Examples of such are home or access networks, wireless home networks,wireless corporate networks, and the like. It should be appreciatedhowever that in general, the systems, methods and techniques disclosedherein will work equally well for other types of communicationsenvironments, networks and/or protocols.

For purposes of explanation, numerous details are set forth in order toprovide a thorough understanding of the present techniques. It should beappreciated however that the present disclosure may be practiced in avariety of ways beyond the specific details set forth herein.Furthermore, while the examples illustrated herein show variouscomponents of the system collocated, it is to be appreciated that thevarious components of the system can be located at distant portions of adistributed network, such as a communications network, node, within aDomain Master, and/or the Internet, or within a dedicated secured,unsecured, and/or encrypted system and/or within a network operation ormanagement device that is located inside or outside the network. As anexample, a Domain Master can also be used to refer to any device, systemor module that manages and/or configures or communicates with any one ormore aspects of the network or communications environment and/ortransceiver(s) and/or stations and/or access point(s) described herein.

Thus, it should be appreciated that the components of the system can becombined into one or more devices, or split between devices, such as atransceiver, an access point, a station, a Domain Master, a networkoperation or management device, a node or collocated on a particularnode of a distributed network, such as a communications network. As willbe appreciated from the following description, and for reasons ofcomputational efficiency, the components of the system can be arrangedat any location within a distributed network without affecting theoperation thereof. For example, the various components can be located ina Domain Master, a node, a domain management device, such as a MIB, anetwork operation or management device, a transceiver(s), a station, anaccess point(s), or some combination thereof. Similarly, one or more ofthe functional portions of the system could be distributed between atransceiver and an associated computing device/system.

Furthermore, it should be appreciated that the various links, includingany communications channel(s)/elements/lines connecting the elements,can be wired or wireless links or any combination thereof, or any otherknown or later developed element(s) capable of supplying and/orcommunicating data to and from the connected elements. The term moduleas used herein can refer to any known or later developed hardware,circuitry, software, firmware, or combination thereof, that is capableof performing the functionality associated with that element. The termsdetermine, calculate, and compute and variations thereof, as used hereinare used interchangeable and include any type of methodology, process,technique, mathematical operational or protocol.

Moreover, while some of the examples described herein are directedtoward a transmitter portion of a transceiver performing certainfunctions, or a receiver portion of a transceiver performing certainfunctions, this disclosure is intended to include corresponding andcomplementary transmitter-side or receiver-side functionality,respectively, in both the same transceiver and/or anothertransceiver(s), and vice versa.

The examples are described in relation to enhanced communications.However, it should be appreciated, that in general, the systems andmethods herein will work equally well for any type of communicationsystem in any environment utilizing any one or more protocols includingwired communications, wireless communications, powerline communications,coaxial cable communications, fiber optic communications, and the like.

The example systems and methods are described in relation to IEEE 802.11and/or Bluetooth® and/or Bluetooth® Low Energy transceivers andassociated communication hardware, software and communication channels.However, to avoid unnecessarily obscuring the present disclosure, thefollowing description omits well-known structures and devices that maybe shown in block diagram form or otherwise summarized.

While the above-described flowcharts have been discussed in relation toa particular sequence of events, it should be appreciated that changesto this sequence can occur without materially effecting the operation ofthe example(s). Additionally, the example techniques illustrated hereinare not limited to the specifically illustrated examples but can also beutilized with the other examples and each described feature isindividually and separately claimable.

The above-described system can be implemented on a wirelesstelecommunications device(s)/system, such an IEEE 802.11 transceiver, orthe like. Examples of wireless protocols that can be used with thistechnology include IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE802.11n, IEEE 802.11ac, IEEE 802.11ad, IEEE 802.11af, IEEE 802.11ah,IEEE 802.11ai, IEEE 802.11aj, IEEE 802.11aq, IEEE 802.11ax, 802.11k,802.11v, & 802.11r, Wi-Fi, LTE, 7G, 5G, Bluetooth®, WirelessHD, WiGig,Wi-Fi, 3GPP, Wireless LAN, WiMAX, DensiFi SIG, Unifi SIG, 3GPP LAA(licensed-assisted access), and the like.

Additionally, the systems, methods and protocols can be implemented toimprove one or more of a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, a hard-wired electronic or logic circuit such as discreteelement circuit, a programmable logic device such as PLD, PLA, FPGA,PAL, a modem, a transmitter/receiver, any comparable means, or the like.In general, any device capable of implementing a state machine that isin turn capable of implementing the methodology illustrated herein canbenefit from the various communication methods, protocols and techniquesaccording to the disclosure provided herein.

Examples of the processors as described herein may include, but are notlimited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm®Snapdragon® 610 and 615 with 7G LTE Integration and 64-bit computing,Apple® A7 processor with 64-bit architecture, Apple® M7 motioncoprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Corte x TMM processors,ARM® Cortex-A and ARM926EJ-S™ processors, Broadcom® AirForceBCM4704/BCM4703 wireless networking processors, the AR7100 WirelessNetwork Processing Unit, other industry-equivalent processors, and mayperform computational functions using any known or future-developedstandard, instruction set, libraries, and/or architecture.

Furthermore, the disclosed methods may be readily implemented insoftware using object or object-oriented software developmentenvironments that provide portable source code that can be used on avariety of computer or workstation platforms. Alternatively, thedisclosed system may be implemented partially or fully in hardware usingstandard logic circuits or VLSI design. Whether software or hardware isused to implement the systems in accordance with the examples isdependent on the speed and/or efficiency requirements of the system, theparticular function, and the particular software or hardware systems ormicroprocessor or microcomputer systems being utilized. Thecommunication systems, methods and protocols illustrated herein can bereadily implemented in hardware and/or software using any known or laterdeveloped systems or structures, devices and/or software by those ofordinary skill in the applicable art from the functional descriptionprovided herein and with a general basic knowledge of the computer andtelecommunications arts.

Moreover, the disclosed techniques may be readily implemented insoftware and/or firmware that can be stored on a storage medium toimprove the performance of a programmed general-purpose computer withthe cooperation of a controller and memory, a special purpose computer,a microprocessor, or the like. In these instances, the systems andmethods can be implemented as program embedded on personal computer suchas an applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated communicationsystem or system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system, such as the hardware and softwaresystems of a communications transceiver.

It is therefore apparent that there have at least been provided systemsand methods for enhancing and improving determinations networkmanagement actions performed in response to a degradation in SLE metricsby determining whether a network device is classified as an edge device.Many alternatives, modifications and variations would be or are apparentto those of ordinary skill in the applicable arts. Accordingly, thisdisclosure is intended to embrace all such alternatives, modifications,equivalents and variations that are within the spirit and scope of thisdisclosure.

What is claimed is:
 1. A system comprising: a plurality of access point(AP) devices configured to provide a wireless network at a site; and anetwork management system comprising: a memory, and one or moreprocessors coupled to the memory and configured to: determine, based onnetwork data received from the plurality of AP devices, a root cause foran occurrence of a network event; determine, based on a determinationthat the root cause of the network event is associated with an AP deviceof the plurality of AP devices, a classification of the AP device as anedge device or non-edge device; determine, based on the network eventand the classification of the AP device, a network management action forthe AP device; and perform the network management action for the APdevice.
 2. The system of claim 1, wherein the AP device is classified asan edge device, wherein the network event comprises a degradation innetwork performance, and wherein the network management action comprisesbypassing a mitigation action with respect to the AP device.
 3. Thesystem of claim 1, wherein the AP device is classified as a non-edgedevice, wherein the network event comprises a degradation in networkperformance, and wherein the network management action comprisesexecuting a mitigation action with respect to the AP device.
 4. Thesystem of claim 1, wherein to determine the classification of the APdevice comprises the one or more processors configured to: determinewhether the AP device is on a periphery of a graph of the plurality ofAP devices; and based on a determination that the AP device is on theperiphery of the graph of the plurality of AP devices, determine thatthe classification of the AP device comprises an edge device.
 5. Thesystem of claim 4, wherein to determine whether the AP device is on theperiphery of the plurality of AP devices comprises the one or moreprocessors configured to: create an AP neighborhood graph having aplurality of nodes, wherein each node represents a corresponding APdevice of the plurality of AP devices and each edge between a first APand a second AP indicates that the first AP can receive a signal fromthe second AP at a power above a predetermined threshold; determine abetweenness centrality value for each node of the plurality of nodes;and determine that the AP device is on the periphery of the APneighborhood graph based on the betweenness centrality value for thenode corresponding to the AP device.
 6. The system of claim 1, whereinto determine the classification of the AP device comprises the one ormore processors configured to: for each AP device of the plurality of APdevices, maintain a join count of a number of times that the respectiveAP device is used by a user equipment (UE) to join the wireless network;and determine that the classification of the AP device comprises an edgedevice based on the join count.
 7. The system of claim 1, wherein todetermine the classification of the AP device comprises the one or moreprocessors configured to: for each AP device of the plurality of APdevices, maintain a leave count of a number of times that a userequipment (UE) associated with the AP device disassociates with the APdevice and leaves the wireless network; and determine that theclassification of the AP device comprises the edge device based on theleave count.
 8. The system of claim 1, wherein to determine theclassification of the AP device comprises the one or more processorsconfigured to: for each AP device, determine a bandwidth utilization ofthe AP device; and determine that the classification of the AP devicecomprises the edge device based on the bandwidth utilization being belowa configurable or predetermined threshold.
 9. The system of claim 1,wherein to determine the network event comprises the one or moreprocessors configured to determine the root cause of the network eventbased on an output of a machine learning model.
 10. A network managementsystem (NMS) that manages a plurality of access point (AP) devices in awireless network, the NMS comprising: a memory, and one or moreprocessors coupled to the memory and configured to: determine, based onnetwork data received from the plurality of AP devices, a root cause foran occurrence of a network event; determine, based on a determinationthat the root cause of the network event is associated with an AP deviceof the plurality of AP devices, a classification of the AP device as anedge device or non-edge device; determine, based on the network eventand the classification of the AP device, a network management action forthe AP device; and perform the network management action for the APdevice.
 11. The NMS of claim 10, wherein AP device is classified as anedge device, wherein the network event comprises a degradation innetwork performance, and wherein the network management action comprisesbypassing a mitigation action with respect to the AP device.
 12. The NMSof claim 10, wherein the AP device is classified as a non-edge device,wherein the network event comprises a degradation in networkperformance, and wherein the network management action comprisesexecuting a mitigation action with respect to the AP device.
 13. The NMSof claim 10, wherein to determine the classification of the AP devicecomprises the one or more processors configured to: determine whetherthe AP device is on a periphery of a graph of the plurality of APdevices; and based on a determination that the AP device is on theperiphery of the graph of the plurality of AP devices, determine thatthe classification of the AP device comprises an edge device.
 14. TheNMS of claim 13, wherein to determine whether the AP device is on theperiphery of the plurality of AP devices comprises the one or moreprocessors configured to: create an AP neighborhood graph having aplurality of nodes, wherein each node represents a corresponding APdevice of the plurality of AP devices and each edge between a noderepresenting first AP and a node representing a second AP indicates thatthe first AP can receive a signal from the second AP at a power above apredetermined threshold; determine a betweenness centrality value foreach node of the plurality of nodes; and determine that the AP device ison the periphery of the AP neighborhood graph based on the betweennesscentrality value for the node corresponding to the AP device.
 15. TheNMS of claim 10, wherein to determine the classification of the APdevice comprises the one or more processors configured to: for each APdevice of the plurality of AP devices, maintain a join count of a numberof times that the respective AP device is used by a user equipment (UE)to join the wireless network; and determine that the classification ofthe AP device comprises an edge device based on the join count.
 16. TheNMS of claim 10, wherein to determine the classification of the APdevice comprises the one or more processors configured to: for each APdevice of the plurality of AP devices, maintain a leave count of anumber of times that a user equipment (UE) associated with the AP devicedisassociates with the AP and leaves the wireless network; and determinethat the classification of the AP device comprises the edge device basedon the leave count.
 17. The NMS of claim 10, wherein to determine theclassification of the AP device comprises the one or more processorsconfigured to: for each AP device, determine a bandwidth utilization ofthe AP device; and determine that the classification of the AP devicecomprises the edge device based on the bandwidth utilization being belowa configurable or predetermined threshold.
 18. The NMS of claim 10,wherein to determine the network event comprises the one or moreprocessors configured to determine the root cause of the network eventbased on an output of a machine learning model.
 19. A method comprising:determining, based on network data received from a plurality of accesspoint (AP) devices, a root cause for an occurrence of a network event;determining, based on a determination that the root cause of the networkevent is associated with an AP device of the plurality of AP devices, aclassification of the AP device as an edge device or non-edge device;determining, based on the network event and the classification of the APdevice, a network management action for the AP device; and performingthe network management action for the AP device.
 20. The method of claim19, wherein determining the classification of the AP device comprisesdetermining that the AP device is an edge device, wherein the networkevent comprises a degradation in network performance, and wherein thenetwork management action comprises bypassing a mitigation action withrespect to the AP device.